We investigate the problem of ranking answers to a database query when many tuples are returned. We adapt and apply principles of probabilistic models from Information Retrieval for structured data. Our proposed solution is domain independent. It leverages data and workload statistics and correlations. Our ranking functions can be further customized for different applications. We present results of preliminary experiments which demonstrate the efficiency as well as the quality of our ranking system. 1.

The popularity of the Web and the large number of documents available in electronic form has motivated the search for hidden knowledge in text collections. Consequently, there is growing research interest in the general topic of text mining. In this paper, we develop a text-mining system by integrating methods from Information Extraction (IE) and Data Mining (Knowledge Discovery from Databases or KDD). By utilizing existing IE and KDD techniques, text-mining systems can be developed relatively rapidly and evaluated on existing text corpora for testing IE systems. We present a general text-mining framework called DiscoTEX which employs an IE module for transforming natural-language documents into structured data and a KDD module for discovering prediction rules from the extracted data. When discovering patterns in extracted text, strict matching of strings is inadequate because textual database entries generally exhibit variations due to typographical errors, misspellings, abbreviations, and other

Variation and noise in textual database entries can prevent text mining algorithms from discovering important regularities. We present two novel methods to cope with this problem: (1) an adaptive approach to "hardening" noisy databases by identifying duplicate records, and (2) mining "soft" association rules. For identifying approximately duplicate records, we present a domain-independent two-level method for improving duplicate detection accuracy based on machine learning. For mining soft matching rules, we introduce an algorithm that discovers association rules by allowing partial matching of items based on a textual similarity metric such as edit distance or cosine similarity. Experimental results on real and synthetic datasets show that our methods outperform traditional techniques for noisy textual databases.

We investigate the problem of ranking the answers to a database query when many tuples are returned. In particular, we present methodologies to tackle the problem for conjunctive and range queries, by adapting and applying principles of probabilistic models from Information Retrieval for structured data. Our solution is domain independent and leverages data and workload statistics and correlations. We evaluate the quality of our approach with a user survey on a real database. Furthermore, we present and experimentally evaluate algorithms to efficiently retrieve the top ranked results, which demonstrate the feasibility of our ranking system.

Text mining concerns the discovery of knowledge from unstructured textual data. One important task is the discovery of rules that relate specific words and phrases. Although existing methods for this task learn traditional logical rules, soft-matching methods that utilize word-frequency information generally work better for textual data. This paper presents a rule induction system, TEXTRISE, that allows for partial matching of text-valued features by combining rule-based and instance-based learning. We present initial experiments applying TEX- TRISE to corpora of book descriptions and patent documents retrieved from the web and compare its results to those of traditional rule and instance based methods. 1

The task of identifying synonymous relations and objects, or synonym resolution, is critical for high-quality information extraction. This paper investigates synonym resolution in the context of unsupervised information extraction, where neither hand-tagged training examples nor domain knowledge is available. The paper presents a scalable, fullyimplemented system that runs in O(KN log N) time in the number of extractions, N, and the maximum number of synonyms per word, K. The system, called Resolver, introduces a probabilistic relational model for predicting whether two strings are co-referential based on the similarity of the assertions containing them. On a set of two million assertions extracted from the Web, Resolver resolves objects with 78 % precision and 68 % recall, and resolves relations with 90 % precision and 35 % recall. Several variations of Resolver’s probabilistic model are explored, and experiments demonstrate that under appropriate conditions these variations can improve F1 by 5%. An extension to the basic Resolver system allows it to handle polysemous names with 97 % precision and 95 % recall on a data set from the TREC corpus.

Abstract. Problem solving with experiences that are recorded in text form requires a mapping from text to structured cases, so that case comparison can provide informed feedback for reasoning. One of the challenges is to acquire an indexing vocabulary to describe cases. We explore the use of machine learning and statistical techniques to automate aspects of this acquisition task. A propositional semantic indexing tool, PSI, which forms its indexing vocabulary from new features extracted as logical combinations of existing keywords, is presented. We propose that such logical combinations correspond more closely to natural concepts and are more transparent than linear combinations. Experiments show PSIderived case representations to have superior retrieval performance to the original keyword-based representations. PSI also has comparable performance to Latent Semantic Indexing, a popular dimensionality reduction technique for text, which unlike PSI generates linear combinations of the original features. 1

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In this paper, we address the problem of query formulation in the context of multi-domain integration of heterogeneous data on the Web. We argue that effectively tackling this problem requires solutions to query specification and refinement, development and organization of domain taxonomies, and designing query templates to incorporate spatial and temporal conditions across multiple domains. We discuss our approaches in designing the query formulation component for InfoMosaic, our proposed framework for multi-domain information integration. 1